.png){kind=icon}
返回
Efficient Deterministic Algorithm for Huge-Sized Noisy Sensor Localization Problems via Canonical Duality Theory
.png){kind=icon}
PRE
AI
DOI：10.1109/TCYB.2019.2891112.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper presents a new deterministic method and a polynomial-time algorithm for solving general huge-sized sensor network localization problems. The problem is first formulated as a nonconvex minimization, which was considered as an NP-hard based on conventional theories. However, by the canonical duality theory, this challenging problem can be equivalently converted into a convex dual problem. By introducing a new optimality measure, a powerful canonical primal-dual interior (CPDI) point algorithm is developed which can solve efficiently huge-sized problems with hundreds of thousands of sensors. The new method is compared with the popular methods in the literature. Results show that the CPDI algorithm is not only faster than the benchmarks but also much more accurate on networks affected by noise on the distances.
Keyword:
Optimization
Noise measurement
Indexes
Minimization
Monitoring
Global Positioning System
Measurement uncertainty
Canonical duality theory
deterministic algorithm
global optimization
sensor network localization (SNL) problem
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
10.5
论文数:
1.1W
被引数:
5.0W
机构
引用论文
.png){kind=icon}
Dynamical Cluster Approximation in Disordered Systems with Magnetic Impurities具有磁性杂质的无序系统中的动力学簇近似
PREAI
分享收藏
查看更多内容